Association graphs in .NET Implementation Code 39 Extended in .NET Association graphs

Association graphs using barcode creation for vs .net control to generate, create barcode 3 of 9 image in vs .net applications. Barcode FAQs Association graph Visual Studio .NET barcode 3 of 9 s embody a methodology which is less restrictive than isomorphism, and which may converge more rapidly. It will converge to a solution which is consistent, but not necessarily optimal (depending, of course, on the criteria for optimality used in any particular application).

The method matches a set of nodes from the model to a set of nodes (extracted) from the image.. Image matching De nition Here, a Code-39 for .NET graph is denoted G = V, P, R , where V represents a set of nodes, P represents a set of unary predicates on nodes, and R represents binary relations between nodes. A predicate is a statement which takes on only the values TRUE or FALSE.

For example let x denote a region in a range image. Then CYLINDRICAL(x) is a predicate which is true or false depending on whether all the pixels in x lie on a cylindrical surface. A binary relation describes a property possessed by a pair of nodes.

It may be considered as a set of ordered pairs R = {(a1 , b1 ), (a2 , b2 ), . . .

(an , bn )}. In most applications, order is important. It is possible to think of a relation as a predicate, since for any given pair, say (ak , bk ), either it is an element of the set R or it is not.

However, it seems more descriptive to use the word relation in this context. Given two graphs, G 1 = V1 , P, R and G 2 = V 2, P, R , we construct the association graph G by:. for each v 1 V1 and v 2 V2 , if v 1 and v 2 have the same properties, construct a node of G labeled (v 1 , v 2 ); if r R and r (v 1 , v 1 ) r (v 2 , v 2 ), connect (v 1 , v 2 ) to (v 1 , v 2 ).. The best match of VS .NET Code 39 Extended G 1 to G 2 is the largest clique of G. Like every other technique in machine vision, we need to ask, How good is this method Some problems arise when attempting to answer that question: Problem 1.

Is the largest clique the best match The largest clique is the largest set of consistent matches. Is this really the best match Problem 2. Computational complexity.

Like the subgraph isomorphism problem, the problem of nding the largest clique is NP-complete. That is, no algorithm is known which can solve this problem in less than exponential time..

An example of using association graphs to match a scene to a model In Fig. 13.2 we i .

NET bar code 39 llustrate an observation in which a segmentation error, oversegmentation, has occurred. That is, regions B and C are actually part of the same region, but due to some measurement or algorithmic error, have been labeled as two separate regions. In this example, the unary predicates are labels spherical, cylindrical, and planar.

Regions A and 1 are spherical, while B, C, D, 2, and 3 are cylindrical. The only candidates for matches are those with the same predicate. So only A can match 1.

We now construct a graph in which all candidate matches are the nodes. We then have the nodes of the association graph, as illustrated in Fig. 13.

3.. 13.3 Graph matching A B C D 1 2 3. Image 1A 2B 3C 2C 3B 2D Model Fig. 13.2.

A rang e camera has observed a scene and segmented it into segments which satisfy the same equation, however, an error has occurred.. Fig. 13.3. Candidate matches. Here is the chall enge: Identify what it means to be consistent determine rA (i, , j, ), or in this example, determine rA (1, A, 2, B), where the compatibility function r has the same meaning as in 10 and the subscript A simply denotes that an association graph is being used. It is often easier to do this by determining what is NOT consistent, and that is a problem-dependent decision. Here, we de ne any two labelings as consistent if they do not involve the same region.

Some example consistencies for this example are rA (1, A, 2, B) = 1.
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